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CENTRALIZED DISSOLVED OXYGEN TRACKING AT WASTEWATER TREATMENT PLANT: NOWY DWOR GDANSKI CASE STUDY
CENTRALIZED DISSOLVED OXYGEN TRACKING AT WASTEWATER TREATMENT PLANT: NOWY DWOR GDANSKI CASE STUDY R. Piotrowski *
M.A. Brdys *
* *
D. Miotke *
*Department of Control Systems Engineering, Gdansk University of Technology, ul. G. Narutowicza 11/12, 80 952 Gdansk, Poland (email: [email protected], [email protected], [email protected]) **Department of Electronic, Electrical and Computer Engineering, College of Engineering and Physical Sciences, University of Birmingham, Birmingham B15 2TT, UK (email:[email protected]) Abstract: The aeration is used in different operations at wastewater treatment plants. The biological processes that are carried out at require sufficiently high dissolved oxygen concentration in order to maintain microorganisms in an activated sludge. The paper proposes a centralized nonlinear model predictive controller of dissolved oxygen tracking and aeration system control. The controller is applied to wastewater treatment plant at Nowy Dwor Gdanski and its performance is compared with the performance of the control system that is currently applied at this site. Copyright © 2010 IFAC Keywords: air, biotechnology, dynamic systems, modelling, nonlinear systems, predictive control, wastewater treatment. 1. INTRODUCTION The biological processes at wastewater treatment plants (WWTP) are very complex, multivariable, nonlinear, timevarying and uncertain. The oxygen delivered into the aerobic zones by the aeration system is a fundamental component for the complex biological processes at WWTP. The denitrification, nitrification and phosphorus removal processes are dependent on concentration of dissolved oxygen (DO) at aerobic zones of the biological reactor. Energy consumption of aeration is the deciding factor of the total energy at WWTP. On-line measurement of DO is one of the most frequently performed measurements at WWTP. Control of DO is very important and difficult activity in the activated sludge processes. As the DO dynamics is nonlinear, typically WWTP operates under high variability of the influent quality and pollutant parameters. Currently, the control of DO concentration at WWTP is based on simple control methods, e.g. PI algorithm with constant parameters that do not aim at all at combining the tracking functions with the energy cost optimization. During the last years several advanced control techniques have been investigated (e.g., Linberg and Carlsson, 1996; Olsson and Newell, 1999; Brdys, et al., 2002; Piotrowski, et al., 2004; Chotkowski, et al., 2005; Piotrowski, et al., 2008; Duzinkiewicz, et al., 2009). The nonlinear predictive controller for DO control was proposed by (Brdys and Konarczak, 2001) and further developed in (Chotkowski, et al., 2005). In these papers the aeration system dynamics is neglected and the system is treated as an actuator with rate and magnitude constraints imposed on the airflow. The others papers (Brdys, et al., 2002; Piotrowski, et al., 2004; Piotrowski, et al., 2008) propose a two level hierarchical controller to track prescribed
DO trajectory. The upper level control unit prescribes trajectories of desired airflows to be delivered into the aerobic biological reactor zones. The lower level controller forces the aeration system to follow these set point trajectories. The paper (Duzinkiewicz, et al., 2009) derives nonlinear hybrid predictive control algorithm for the lower level controller. It is directly based on the nonlinear hybrid dynamics and logical formulation of the switching constraint. The drinking water distribution systems (DWDS) and wastewater treatment systems are classified as members of a general class of Critical Infrastructure Systems (Memorandum of Understanding, 2008). The reliable, high performance and secure operation of these infrastructures is very essential for the society. The problem considered in the paper is very relevant to the planned activities of the Working Group 1: Intelligent System-Theoretic Approaches for Critical Infrastructure Systems within new EU Cost Action IC0806 – IntelliCIS (Memorandum of Understanding, 2008). In this paper a centralized nonlinear model predictive controller (NMPC) controller for DO tracking and aeration system control is designed and applied to WWTP at Nowy Dwor Gdanski (NDG). Two models: biological WWTP and aeration system are modelled and investigated. The DO at biological reactor is the control input and the speed of the blowers at aeration system is the output. The control system is designed for NDG case study plant. 2. WASTEWATER TREATMENT PLANT AT NOWY DWOR GDANSKI 2.1 Structure of the WWTP WWTP at NDG is located in northern Poland. The mechanical and biological processes are used for wastewater
Internal recirculation
Waste from mechanical treatment Predenitrification zone 152 m3
Anaerobic zone
Anoxic zone 1
Aerobic zone 1
1485 m3
1622 m3
Anoxic zone 2
Aerobic zone 2
1485 m3
1622 m3
455 m3
Secondary settler 1
401 m3
Effluent Secondary settler 2
401 m3
Internal recirculation Waste sludge
Sludge recirculation (external recirculation) Aeration system
Fig. 1. Layout of biological WWTP at NDG.
Activated sludge models (ASM) of the biological reactor are the most popular mathematical description of the biological processes at WWTP. Family of ASM consists of: ASM1, ASM2, ASM2d, ASM3 and ASM3 Bio P. In this paper biological processes were modelled by applying ASM2d model (Henze, et al., 1995). This model consists of twenty one state variables and twenty kinetic and stoichiometric parameters. ASM2d model was calibrated based on real data sets from WWTP at NDG. Additionally, determinated values of all recirculations, values of inputs: inflow (Qin), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP). Verification of the modelling results were satisfactory and next they were used for control purposes. 3. AERATION SYSTEM AT NOWY DWOR GDANSKI 3.1 Description of the aeration system The DO concentration is a principal manipulated variable in WWTP. On line sensors for DO measurements are cheap and generally available. Oxygen is supplied by aeration system and it is the main component for biological processes.
D p dopen = 2, 25 kPa . Below this value diffuser is closed. This is illustrated in Fig. 2 where the aeration system 1 is shown. Main pipe
Aerobic zone 1
pc
l c=44m, d c=0,15m
Qc
2.2 Model of the WWTP
airflow, pressure drop across the blower and motor rotational speed, respectively. The blowers can run over the speed range 1500-4800 rpm. The speed is forced by an inverter controlled induction motor coupled with the blower. The blower forces air to the main pipe (length lc=44m, diameter dc=0,15m). Next, the airflow is divided among aeration segment unit. This unit consists of: pipe dip in aerobic zone (length lp=12m, diameter dp=0,15m) and diffuser system. The diffuser system is composed of a number of diffusers in parallel located at the aerobic zone bottom floor and connected through a network of secondary pipes. The membrane diffusers are located inside the tank at a level h=3,9m. There are n=408 diffusers. The relationship between airflow through the diffuser and pressure drop across the diffuser is nonlinear. The pressure drop across the diffuser should not be smaller then
lp =12m, dp=0,15m
The first zone where phosphorus is released is anaerobic. Next, technological line is divided into two identical separate parts. In the paper only one of the technological lines is considered. In the anoxic zone denitrification processes are conducted. Additionally, recirculated activated sludge from aerobic zone is provided. The next unit of the technological scheme is an aerobic zone where nitrification processes takes place. This zone is aerated by aeration system. The wastewater and activated sludge are separated in secondary settler. The activated sludge is recirculated to activated sludge predenitrification zone. The excess waste sludge is removed, chemically stabilized and stored. The Rozewo Canal is the effluent receiver. WWTP at NDG is a typical WWTP with a continuous flow throughout the plant as opposed to the sequential batch reactor (SBR) WWTP.
Additionally, sewage with activated sludge is mixed what helps in treating the sewage process. It is essential to maintain the DO at the right level. The different ways of aerations are applied: high purity oxygen aeration, mechanical aeration and diffused aeration (Mueller, et al., 2002). The last method is used at NDG WWTP. There are two identical and independent aeration systems and the first one is described and modelled in the paper. Air to the aerobic zone is supplied by the blower station composed of two variable speed blowers. The first one supplies air to aerobic zone 1 and the second blower supplies air to aerobic zone 2. The blowers are described by nonlinear function Qb = fb ( D pb ,nb ) , where Qb , D p b , nb are the blower output
Variable–speed blower
treatment. In this paper the biological part of WWTP is considered. Different biological processes are applied for the removal of organic substrate, nitrogen and phosphorus. Activated sludge method with nitrification, denitrification and phosphorus removal processes is utilized. A technological layout of the plant is illustrated in Fig. 1.
Aeration segment unit
Q air Diffuser system
Fig. 2. The aeration system for aerobic zone 1. 3.2 Model of the aeration system The detailed model of the aeration system is described in Piotrowski, et al. (2008). This model is applied to the case study aeration system. The blower is a flow source with relationship between airflow through the blower Q b , pressure drop across a blower D p b and blower speed n b and it is linearized as:
Qb = - 1, 291 D p + b 0,323 n -b 60,966
(1)
D p b= p b- p a
(2)
where p a is the atmospheric pressure. The main pipe dynamics is described as the fluid-flow capacitance C c :
dp c
1 = dt Cc
( Qb - Q c ) ;
Vc
Cc=
pc
; Vc=
p d c2 4
lc
(3)
where p c , Q c , V c are the pressure at the main pipe, main pipe airflow and main pipe volume, respectively.
D p h= r g h
where D p h ,r , g are the hydrostatic pressure drop across, wastewater density and acceleration due to gravity, respectively; because h is constants then D p h is also constant.
The above differential equation is nonlinear. As the fluidflow resistance of the main pipe is negligible and p b = p c .
An electrical analogy of the aeration system model at NDG WWTP is illustrated in Fig. 3. pb Q b
Connecting the blower to main pipe is modelled as fluid-flow resistance R r :
( )
D p r = R r Q b Q b= 2,59 e - 7 Q b2
Rr
D pr
(4) D pb
where D p r is the pressure drop across the blower – main
The resistance of pipe R g is given by:
( )
D p g = R g Q c Q c= 2,374 e - 7 Q c2
(5)
where D p g , Q g are the pressure drop across of pipe and
Diffuser resistance Rd can be described by:
D p d- D n = Rd 0
p dopen
Cd
Q ai r
D pd
This model was verified based on real data sets from NDG WWTP and the data available from technical documentation of the aeration system elements. The aeration system is nonlinear with fast dynamics. The analogous results were obtained for others aeration systems (Krawczyk, et al., 2007; Piotrowski, et al., 2008). Neglecting the aeration system dynamics yields the steady state model:
for D p d‡ D
p dopen
(6)
otherwise
pressure drop across at the diffuser system, respectively.
d
p dopen
)
for D ‡pDd
p dopen
otherwise
0
(7)
where a a = 4 ,5432 . Finally, the dynamics of the diffusers is modelled as:
Rd C d
dQ air dt
+ Q air = Q c
(8)
Additionally, a pressure balance over an open diffuser yields:
p c = D p r+ D p + d D
ph
(see (3))
dQair =0 dt
(see (8))
that is applied in the next section to design the DO tracking system. 4. CONTROL SYSTEM DESIGN 4.1 DO control at NDG
Nonlinear equation (6) was lineralized, to produce:
(D p - D
dpc =0 dt and
where Q air , D p d are the airflow into aerobic zone and
and
Cc
Rd
D pg
Fig. 3. Electrical analogy of the aeration system model.
airflow through the pipe, respectively.
Q air
Rg
Q b -Q c
pa
The next element of aeration system is aeration segment unit which consists of: pipe and diffusers system.
n a d =
pc Q c
D ph
pipe connection.
Q air
(10)
(9)
To control the DO concentration very often simple control technologies are used. The control methods include manual control, rule based control, simple PLC techniques with PI controllers. Currently at NDG aeration system only the DO concentration S0 in each aeration zone is on-line measured. Control of S0 is realized by direct PI controller with fixed parameters and applied by PLC. During daily operation the S0 set point of is fixed. This value is chosen arbitrary by observation of operational conditions. Under a high variability of influent flow and concentration of pollutant, the plant operating point can vary, considerably. Since the DO dynamics is highly nonlinear, a fixed parameter linear controller is not able to maintain a satisfactory tracking performance under the full range of operating conditions and requires on line parameter tuning. The result of S0 control currently used at NDG WWTP is illustrated in Fig. 4.
limit, respiration oxygen transfer function and oxygen transfer coefficient, respectively. The last term in (11) denotes the respiration rate at the aerobic zone. In Chotkowski, et al. (2005) it was shown that R r (t ) changes much more slowly than So (t ) . It was a key finding that made possible very efficient application of the NMPC. Namely, it was proposed apply in NMPC at time instant t a constant value prediction of R r (t ) over the
Fig. 4. Results of DO control at aerobic zone 1.
prediction horizon [t , t + H p ] entirely based on an estimate of It can be clearly seen that the DO reference concent ration R r (t - 1) at t. The latter can be easily obtained based on the tracking is poor. Moreover, the control doesn’t account for the tracking economic costs at all. Hence, there is a room also model (11) supplied with the measurements S m (t - T ) and o for considerable savings of the electrical energy consumption m So (t ) of So (t - T ) and So (t ) , where T denotes sampling cost. interval. The NMPC performance function is given by: 4.2 Nonlinear Model Predictive Control
The model predictive control (MPC) technology is an attractive method for dynamic optimizing control. The MPC optimizer enables for direct incorporation of the constraints in the control problem into the optimization task at each time step, what is a great advantage of this technology. During last years many industrial applications of control systems have utilized the MPC technology. A structure of new control system is shown in Fig. 5. Qin, COD, TN, TP
Plant So ref S0
NMPC Controller
nb
Aeration System nb → Qair
Biological WWTP Qair → S0
where
k La ( Qair ) = a Qair ,
Hp
i =1
(12)
i =1
The first term in (12) represents the tracking error. The second term describes the control cost. The weights h is tuning knob used to achieve a desired compromise between the tracking error and the cost of the energy used due to pumping the air.
Qair ( k + i | k ) - Qair ( k+ i- 1 | k )£
D Qair ; =i
1,...,H P
(13) (14)
min max where Qair = 370 m 3 h , Qair = 1436 m 3 h , D Qair = 319 m 3 h .
= k La ( Qair ( t ) ) ( S osat - S o ( t ) )-
S osat = 8 ,63 g / m3 ,
2
}
min max Qair ; =i 1,...,H P £ Qair ( k+ i | k )£ Qair
S0
The controller objectives are to force the S0 in the aerobic zone to follow the prescribed reference trajectory S0ref and the same time to minimize the electrical energy cost due to blowing the air into the zone. The blower speed nb is the control handle. The air is distributed into the aerobic zone by aeration system (see section 3). This system is coupled with biological WWTP (see section 2). The variable that links nb with the So is the airflow appearing in both models. The Qin, COD, TN and TP are disturbing input signals. The NMPC controller is to be designed as follows. The new control system is centralized as opposed to the hierarchical control system which is considered in the previous paper by Piotrowski, et al. (2008). The NMPC algorithm uses model of DO for its prediction purposes. A dynamics of S0 is described by the nonlinear differential equation (Olsson and Newell, 1999): dt
{
The magnitude and rate constraints are as follows:
Fig. 5. Control system layout.
dS0 ( t )
Hp
J = ∑ So ( k + i | k ) - S oref ( k+ i | k ) + h ∑ pc ( k+ i | k )
So ( t )
K o + So (t )
Rr ( t )
K o = 0.2 g / m3 , 3
(11) R r (t ) ,
a = 0 ,00267 1 / m denote dissolved oxygen saturation concentration, Monod’s constant of DO
5. CASE STUDY RESULTS AND DISCUSSION The commercial simulation package SIMBA (Simba, 2005) was applied to modeling a biological part of the WWTP. The model of aeration system was implemented in MATLAB environment. Next, both of models were coupled (see Fig. 5). The SQP solver was used to solve the NMPC optimization task. The NMPC parameters were determined experimentally as Hp=5 steps and sampling time T=5 min. The control system was simulated based on real data records over 1 day under the influent scenarios of large variations as shown in Figs. 6-7. The respiration R r (t ) at aerobic zone 1 is illustrated in Fig. 8 and the zoomed parts in Fig. 9. It can be seen that the respiration being the disturbance for NMPC controller is time-varying, hence an open loop optimisation is not viable and the NMPC feedback mechanism is essential. Simulation results of aerobic zone 1 for optimized NMPC control are presented in Figs. 10-13. Results for second aerobic zone are very similar. The results of the DO tracking are very good (Fig. 10 and Fig. 11). The variable speed blower operation is illustrated in Figs. 12-13. The wide speed range 1500-4800 rpm of the variable speed blower helps to follow the DO reference trajectory with very good accuracy.
Fig. 6. Inflow into NDG WWTP.
Fig. 10. DO control at aerobic zone 1.
Fig. 7. Influent Chemical Oxygen Demand.
Fig. 11. DO control at aerobic zone 1 – zoomed parts.
Fig. 8. Respiration at aerobic zone 1.
Fig. 9. Respiration at aerobic zone 1 – zoomed parts.
Fig. 12. Speed of the variable speed blower.
Fig. 13. Speed of the variable speed blower – zoomed parts.
Next, the system was analyzed regarding different values of h (see (12)). The results show a good performance of the control system (Fig. 14). Zoomed parts of the DO tracking are presented in Fig. 15. Control results are significantly better than performance of the control system that is currently used at NDG (see Fig. 4). Speed of the variable speed blower is shown in Fig. 16. Increasing h slightly influences the DO tracking performance but it noticeably decreases the pumping costs due to resulting lowering the blower average speed.
treatment plant. A novel centralised nonlinear model predictive controller has been derived. Its properties and tracking performance have been investigated by simulation based on real data sets from Nowy Dwor Gdanski case study plant. Promising results have been observed. ACKNOWLEDGMENT This work was supported by the COST Action IC0806: Intelligent Monitoring, Control and Security of Critical Infrastructure Systems (IntelliCIS). The authors wish to express their thanks for the support. REFERENCES
Fig. 14. DO control at aerobic zone 1.
Fig. 15. DO control at aerobic zone 1 – zoomed parts.
Fig. 16. Speed of the variable speed blower. 6. CONCLUSIONS The paper has addressed the optimised tracking of a reference trajectory of dissolved oxygen concentration at wastewater
Brdys, M.A., Chotkowski, W., Duzinkiewicz, K., Konarczak, K., and Piotrowski, R. (2002). Two-level dissolved oxygen control for activated sludge processes. Proc. of the 15th IFAC World Congress, Barcelona, July 21-26. Brdys, M.A., Grochowski, M., Gminski, T., Konarczak, K., and Drewa, M. (2008). Hierarchical predictive control of integrated wastewater systems. Control Engineering Practice, Vol. 16, Issue 6, pp. 751-767. Chotkowski, W., Brdys, M.A., and Konarczak, K. (2005). Dissolved oxygen control for activated sludge processes. International Journal of Systems Science, Vol. 36, No. 12, 727-736. COST ACTION IC0806-IntelliCIS (2008). Memorandum of Understanding, 7th Framework Program, European Commission, https://www.cost.esf.org. Henze, M., Gujer, W., Mino, W., Matsuo, T., Wentzel, M.C., and Marais, G.v.R. (1995). Activated Sludge Model No. 2. Scientific and Technical Report No.3, IAWQ, Londyn. Duzinkiewicz, K., Brdys, M.A., Kurek, W., and Piotrowski, R. (2009). Genetic hybrid predictive controller for optimised dissolved oxygen tracking at lower control level. IEEE Transactions on Control Systems Technology, Vol. 17, No. 5, pp. 1183-1192. Krawczyk, W., Piotrowski, R., Brdys, M.A., and Chotkowski, W. (2007). Modelling and identification of aeration systems for model predictive control of dissolved oxygen – Swarzewo wastewater treatment plant case study. Proc. of the 10th IFAC Symposium on Computer Applications in Biotechnology, Cancun, June 04-06. Mueller, J.A., Boyle, W.C., and Pöpel, H.J. (2002). Aeration: Principles and Practice. CRC Press, Boca Raton. Olsson, G., and Newell, R. (1999). Wastewater Treatment Systems. Modelling, Diagnosis and Control. IWA Publishing, London. Piotrowski, R., Brdys, M.A., Konarczak, K., Duzinkiewicz K., and Chotkowski, W. (2008). Hierarchical dissolved oxygen control for activated sludge processes. Control Engineering Practice, Vol. 16, No. 1, pp. 114-131. Piotrowski, R., Duzinkiewicz, K., and Brdys, M.A. (2004). Dissolved oxygen tracking and control of blowers at fast time scale. Proc. of the 10th IFAC Symposium Large Scale Systems: Theory and Applications, Osaka, July 26-28. Simba (2005). User’s guide, http://simba.ifak.eu/simba/.
M.A. Brdys *
* *
D. Miotke *
*Department of Control Systems Engineering, Gdansk University of Technology, ul. G. Narutowicza 11/12, 80 952 Gdansk, Poland (email: [email protected], [email protected], [email protected]) **Department of Electronic, Electrical and Computer Engineering, College of Engineering and Physical Sciences, University of Birmingham, Birmingham B15 2TT, UK (email:[email protected]) Abstract: The aeration is used in different operations at wastewater treatment plants. The biological processes that are carried out at require sufficiently high dissolved oxygen concentration in order to maintain microorganisms in an activated sludge. The paper proposes a centralized nonlinear model predictive controller of dissolved oxygen tracking and aeration system control. The controller is applied to wastewater treatment plant at Nowy Dwor Gdanski and its performance is compared with the performance of the control system that is currently applied at this site. Copyright © 2010 IFAC Keywords: air, biotechnology, dynamic systems, modelling, nonlinear systems, predictive control, wastewater treatment. 1. INTRODUCTION The biological processes at wastewater treatment plants (WWTP) are very complex, multivariable, nonlinear, timevarying and uncertain. The oxygen delivered into the aerobic zones by the aeration system is a fundamental component for the complex biological processes at WWTP. The denitrification, nitrification and phosphorus removal processes are dependent on concentration of dissolved oxygen (DO) at aerobic zones of the biological reactor. Energy consumption of aeration is the deciding factor of the total energy at WWTP. On-line measurement of DO is one of the most frequently performed measurements at WWTP. Control of DO is very important and difficult activity in the activated sludge processes. As the DO dynamics is nonlinear, typically WWTP operates under high variability of the influent quality and pollutant parameters. Currently, the control of DO concentration at WWTP is based on simple control methods, e.g. PI algorithm with constant parameters that do not aim at all at combining the tracking functions with the energy cost optimization. During the last years several advanced control techniques have been investigated (e.g., Linberg and Carlsson, 1996; Olsson and Newell, 1999; Brdys, et al., 2002; Piotrowski, et al., 2004; Chotkowski, et al., 2005; Piotrowski, et al., 2008; Duzinkiewicz, et al., 2009). The nonlinear predictive controller for DO control was proposed by (Brdys and Konarczak, 2001) and further developed in (Chotkowski, et al., 2005). In these papers the aeration system dynamics is neglected and the system is treated as an actuator with rate and magnitude constraints imposed on the airflow. The others papers (Brdys, et al., 2002; Piotrowski, et al., 2004; Piotrowski, et al., 2008) propose a two level hierarchical controller to track prescribed
DO trajectory. The upper level control unit prescribes trajectories of desired airflows to be delivered into the aerobic biological reactor zones. The lower level controller forces the aeration system to follow these set point trajectories. The paper (Duzinkiewicz, et al., 2009) derives nonlinear hybrid predictive control algorithm for the lower level controller. It is directly based on the nonlinear hybrid dynamics and logical formulation of the switching constraint. The drinking water distribution systems (DWDS) and wastewater treatment systems are classified as members of a general class of Critical Infrastructure Systems (Memorandum of Understanding, 2008). The reliable, high performance and secure operation of these infrastructures is very essential for the society. The problem considered in the paper is very relevant to the planned activities of the Working Group 1: Intelligent System-Theoretic Approaches for Critical Infrastructure Systems within new EU Cost Action IC0806 – IntelliCIS (Memorandum of Understanding, 2008). In this paper a centralized nonlinear model predictive controller (NMPC) controller for DO tracking and aeration system control is designed and applied to WWTP at Nowy Dwor Gdanski (NDG). Two models: biological WWTP and aeration system are modelled and investigated. The DO at biological reactor is the control input and the speed of the blowers at aeration system is the output. The control system is designed for NDG case study plant. 2. WASTEWATER TREATMENT PLANT AT NOWY DWOR GDANSKI 2.1 Structure of the WWTP WWTP at NDG is located in northern Poland. The mechanical and biological processes are used for wastewater
Internal recirculation
Waste from mechanical treatment Predenitrification zone 152 m3
Anaerobic zone
Anoxic zone 1
Aerobic zone 1
1485 m3
1622 m3
Anoxic zone 2
Aerobic zone 2
1485 m3
1622 m3
455 m3
Secondary settler 1
401 m3
Effluent Secondary settler 2
401 m3
Internal recirculation Waste sludge
Sludge recirculation (external recirculation) Aeration system
Fig. 1. Layout of biological WWTP at NDG.
Activated sludge models (ASM) of the biological reactor are the most popular mathematical description of the biological processes at WWTP. Family of ASM consists of: ASM1, ASM2, ASM2d, ASM3 and ASM3 Bio P. In this paper biological processes were modelled by applying ASM2d model (Henze, et al., 1995). This model consists of twenty one state variables and twenty kinetic and stoichiometric parameters. ASM2d model was calibrated based on real data sets from WWTP at NDG. Additionally, determinated values of all recirculations, values of inputs: inflow (Qin), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP). Verification of the modelling results were satisfactory and next they were used for control purposes. 3. AERATION SYSTEM AT NOWY DWOR GDANSKI 3.1 Description of the aeration system The DO concentration is a principal manipulated variable in WWTP. On line sensors for DO measurements are cheap and generally available. Oxygen is supplied by aeration system and it is the main component for biological processes.
D p dopen = 2, 25 kPa . Below this value diffuser is closed. This is illustrated in Fig. 2 where the aeration system 1 is shown. Main pipe
Aerobic zone 1
pc
l c=44m, d c=0,15m
Qc
2.2 Model of the WWTP
airflow, pressure drop across the blower and motor rotational speed, respectively. The blowers can run over the speed range 1500-4800 rpm. The speed is forced by an inverter controlled induction motor coupled with the blower. The blower forces air to the main pipe (length lc=44m, diameter dc=0,15m). Next, the airflow is divided among aeration segment unit. This unit consists of: pipe dip in aerobic zone (length lp=12m, diameter dp=0,15m) and diffuser system. The diffuser system is composed of a number of diffusers in parallel located at the aerobic zone bottom floor and connected through a network of secondary pipes. The membrane diffusers are located inside the tank at a level h=3,9m. There are n=408 diffusers. The relationship between airflow through the diffuser and pressure drop across the diffuser is nonlinear. The pressure drop across the diffuser should not be smaller then
lp =12m, dp=0,15m
The first zone where phosphorus is released is anaerobic. Next, technological line is divided into two identical separate parts. In the paper only one of the technological lines is considered. In the anoxic zone denitrification processes are conducted. Additionally, recirculated activated sludge from aerobic zone is provided. The next unit of the technological scheme is an aerobic zone where nitrification processes takes place. This zone is aerated by aeration system. The wastewater and activated sludge are separated in secondary settler. The activated sludge is recirculated to activated sludge predenitrification zone. The excess waste sludge is removed, chemically stabilized and stored. The Rozewo Canal is the effluent receiver. WWTP at NDG is a typical WWTP with a continuous flow throughout the plant as opposed to the sequential batch reactor (SBR) WWTP.
Additionally, sewage with activated sludge is mixed what helps in treating the sewage process. It is essential to maintain the DO at the right level. The different ways of aerations are applied: high purity oxygen aeration, mechanical aeration and diffused aeration (Mueller, et al., 2002). The last method is used at NDG WWTP. There are two identical and independent aeration systems and the first one is described and modelled in the paper. Air to the aerobic zone is supplied by the blower station composed of two variable speed blowers. The first one supplies air to aerobic zone 1 and the second blower supplies air to aerobic zone 2. The blowers are described by nonlinear function Qb = fb ( D pb ,nb ) , where Qb , D p b , nb are the blower output
Variable–speed blower
treatment. In this paper the biological part of WWTP is considered. Different biological processes are applied for the removal of organic substrate, nitrogen and phosphorus. Activated sludge method with nitrification, denitrification and phosphorus removal processes is utilized. A technological layout of the plant is illustrated in Fig. 1.
Aeration segment unit
Q air Diffuser system
Fig. 2. The aeration system for aerobic zone 1. 3.2 Model of the aeration system The detailed model of the aeration system is described in Piotrowski, et al. (2008). This model is applied to the case study aeration system. The blower is a flow source with relationship between airflow through the blower Q b , pressure drop across a blower D p b and blower speed n b and it is linearized as:
Qb = - 1, 291 D p + b 0,323 n -b 60,966
(1)
D p b= p b- p a
(2)
where p a is the atmospheric pressure. The main pipe dynamics is described as the fluid-flow capacitance C c :
dp c
1 = dt Cc
( Qb - Q c ) ;
Vc
Cc=
pc
; Vc=
p d c2 4
lc
(3)
where p c , Q c , V c are the pressure at the main pipe, main pipe airflow and main pipe volume, respectively.
D p h= r g h
where D p h ,r , g are the hydrostatic pressure drop across, wastewater density and acceleration due to gravity, respectively; because h is constants then D p h is also constant.
The above differential equation is nonlinear. As the fluidflow resistance of the main pipe is negligible and p b = p c .
An electrical analogy of the aeration system model at NDG WWTP is illustrated in Fig. 3. pb Q b
Connecting the blower to main pipe is modelled as fluid-flow resistance R r :
( )
D p r = R r Q b Q b= 2,59 e - 7 Q b2
Rr
D pr
(4) D pb
where D p r is the pressure drop across the blower – main
The resistance of pipe R g is given by:
( )
D p g = R g Q c Q c= 2,374 e - 7 Q c2
(5)
where D p g , Q g are the pressure drop across of pipe and
Diffuser resistance Rd can be described by:
D p d- D n = Rd 0
p dopen
Cd
Q ai r
D pd
This model was verified based on real data sets from NDG WWTP and the data available from technical documentation of the aeration system elements. The aeration system is nonlinear with fast dynamics. The analogous results were obtained for others aeration systems (Krawczyk, et al., 2007; Piotrowski, et al., 2008). Neglecting the aeration system dynamics yields the steady state model:
for D p d‡ D
p dopen
(6)
otherwise
pressure drop across at the diffuser system, respectively.
d
p dopen
)
for D ‡pDd
p dopen
otherwise
0
(7)
where a a = 4 ,5432 . Finally, the dynamics of the diffusers is modelled as:
Rd C d
dQ air dt
+ Q air = Q c
(8)
Additionally, a pressure balance over an open diffuser yields:
p c = D p r+ D p + d D
ph
(see (3))
dQair =0 dt
(see (8))
that is applied in the next section to design the DO tracking system. 4. CONTROL SYSTEM DESIGN 4.1 DO control at NDG
Nonlinear equation (6) was lineralized, to produce:
(D p - D
dpc =0 dt and
where Q air , D p d are the airflow into aerobic zone and
and
Cc
Rd
D pg
Fig. 3. Electrical analogy of the aeration system model.
airflow through the pipe, respectively.
Q air
Rg
Q b -Q c
pa
The next element of aeration system is aeration segment unit which consists of: pipe and diffusers system.
n a d =
pc Q c
D ph
pipe connection.
Q air
(10)
(9)
To control the DO concentration very often simple control technologies are used. The control methods include manual control, rule based control, simple PLC techniques with PI controllers. Currently at NDG aeration system only the DO concentration S0 in each aeration zone is on-line measured. Control of S0 is realized by direct PI controller with fixed parameters and applied by PLC. During daily operation the S0 set point of is fixed. This value is chosen arbitrary by observation of operational conditions. Under a high variability of influent flow and concentration of pollutant, the plant operating point can vary, considerably. Since the DO dynamics is highly nonlinear, a fixed parameter linear controller is not able to maintain a satisfactory tracking performance under the full range of operating conditions and requires on line parameter tuning. The result of S0 control currently used at NDG WWTP is illustrated in Fig. 4.
limit, respiration oxygen transfer function and oxygen transfer coefficient, respectively. The last term in (11) denotes the respiration rate at the aerobic zone. In Chotkowski, et al. (2005) it was shown that R r (t ) changes much more slowly than So (t ) . It was a key finding that made possible very efficient application of the NMPC. Namely, it was proposed apply in NMPC at time instant t a constant value prediction of R r (t ) over the
Fig. 4. Results of DO control at aerobic zone 1.
prediction horizon [t , t + H p ] entirely based on an estimate of It can be clearly seen that the DO reference concent ration R r (t - 1) at t. The latter can be easily obtained based on the tracking is poor. Moreover, the control doesn’t account for the tracking economic costs at all. Hence, there is a room also model (11) supplied with the measurements S m (t - T ) and o for considerable savings of the electrical energy consumption m So (t ) of So (t - T ) and So (t ) , where T denotes sampling cost. interval. The NMPC performance function is given by: 4.2 Nonlinear Model Predictive Control
The model predictive control (MPC) technology is an attractive method for dynamic optimizing control. The MPC optimizer enables for direct incorporation of the constraints in the control problem into the optimization task at each time step, what is a great advantage of this technology. During last years many industrial applications of control systems have utilized the MPC technology. A structure of new control system is shown in Fig. 5. Qin, COD, TN, TP
Plant So ref S0
NMPC Controller
nb
Aeration System nb → Qair
Biological WWTP Qair → S0
where
k La ( Qair ) = a Qair ,
Hp
i =1
(12)
i =1
The first term in (12) represents the tracking error. The second term describes the control cost. The weights h is tuning knob used to achieve a desired compromise between the tracking error and the cost of the energy used due to pumping the air.
Qair ( k + i | k ) - Qair ( k+ i- 1 | k )£
D Qair ; =i
1,...,H P
(13) (14)
min max where Qair = 370 m 3 h , Qair = 1436 m 3 h , D Qair = 319 m 3 h .
= k La ( Qair ( t ) ) ( S osat - S o ( t ) )-
S osat = 8 ,63 g / m3 ,
2
}
min max Qair ; =i 1,...,H P £ Qair ( k+ i | k )£ Qair
S0
The controller objectives are to force the S0 in the aerobic zone to follow the prescribed reference trajectory S0ref and the same time to minimize the electrical energy cost due to blowing the air into the zone. The blower speed nb is the control handle. The air is distributed into the aerobic zone by aeration system (see section 3). This system is coupled with biological WWTP (see section 2). The variable that links nb with the So is the airflow appearing in both models. The Qin, COD, TN and TP are disturbing input signals. The NMPC controller is to be designed as follows. The new control system is centralized as opposed to the hierarchical control system which is considered in the previous paper by Piotrowski, et al. (2008). The NMPC algorithm uses model of DO for its prediction purposes. A dynamics of S0 is described by the nonlinear differential equation (Olsson and Newell, 1999): dt
{
The magnitude and rate constraints are as follows:
Fig. 5. Control system layout.
dS0 ( t )
Hp
J = ∑ So ( k + i | k ) - S oref ( k+ i | k ) + h ∑ pc ( k+ i | k )
So ( t )
K o + So (t )
Rr ( t )
K o = 0.2 g / m3 , 3
(11) R r (t ) ,
a = 0 ,00267 1 / m denote dissolved oxygen saturation concentration, Monod’s constant of DO
5. CASE STUDY RESULTS AND DISCUSSION The commercial simulation package SIMBA (Simba, 2005) was applied to modeling a biological part of the WWTP. The model of aeration system was implemented in MATLAB environment. Next, both of models were coupled (see Fig. 5). The SQP solver was used to solve the NMPC optimization task. The NMPC parameters were determined experimentally as Hp=5 steps and sampling time T=5 min. The control system was simulated based on real data records over 1 day under the influent scenarios of large variations as shown in Figs. 6-7. The respiration R r (t ) at aerobic zone 1 is illustrated in Fig. 8 and the zoomed parts in Fig. 9. It can be seen that the respiration being the disturbance for NMPC controller is time-varying, hence an open loop optimisation is not viable and the NMPC feedback mechanism is essential. Simulation results of aerobic zone 1 for optimized NMPC control are presented in Figs. 10-13. Results for second aerobic zone are very similar. The results of the DO tracking are very good (Fig. 10 and Fig. 11). The variable speed blower operation is illustrated in Figs. 12-13. The wide speed range 1500-4800 rpm of the variable speed blower helps to follow the DO reference trajectory with very good accuracy.
Fig. 6. Inflow into NDG WWTP.
Fig. 10. DO control at aerobic zone 1.
Fig. 7. Influent Chemical Oxygen Demand.
Fig. 11. DO control at aerobic zone 1 – zoomed parts.
Fig. 8. Respiration at aerobic zone 1.
Fig. 9. Respiration at aerobic zone 1 – zoomed parts.
Fig. 12. Speed of the variable speed blower.
Fig. 13. Speed of the variable speed blower – zoomed parts.
Next, the system was analyzed regarding different values of h (see (12)). The results show a good performance of the control system (Fig. 14). Zoomed parts of the DO tracking are presented in Fig. 15. Control results are significantly better than performance of the control system that is currently used at NDG (see Fig. 4). Speed of the variable speed blower is shown in Fig. 16. Increasing h slightly influences the DO tracking performance but it noticeably decreases the pumping costs due to resulting lowering the blower average speed.
treatment plant. A novel centralised nonlinear model predictive controller has been derived. Its properties and tracking performance have been investigated by simulation based on real data sets from Nowy Dwor Gdanski case study plant. Promising results have been observed. ACKNOWLEDGMENT This work was supported by the COST Action IC0806: Intelligent Monitoring, Control and Security of Critical Infrastructure Systems (IntelliCIS). The authors wish to express their thanks for the support. REFERENCES
Fig. 14. DO control at aerobic zone 1.
Fig. 15. DO control at aerobic zone 1 – zoomed parts.
Fig. 16. Speed of the variable speed blower. 6. CONCLUSIONS The paper has addressed the optimised tracking of a reference trajectory of dissolved oxygen concentration at wastewater
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